The current predictive models advocated by the Predictive Analytics Reporting (PAR) Framework for higher-education institutions are based on regular term structures, such as semester- or quarter-based academic calendars. These predictive models also rely on a fixed set of predictors common across many institutions. U.S. Patent App. Pub. No. 2015/0193699 A1, titled “Data-Adaptive Insight and Action Platform for Higher Education,” published on Jul. 9, 2015, describes a data-adaptive approach to model building that leverages all available student-data footprints at a particular institution.
However, in higher education, growing number of institutions serve a diverse group of students, such as working adults and students who want more flexibility in schedule through online learning. Furthermore, these institutions are introducing competency-based learning (CBL) as an alternative to time-based learning models to cater to students with varying academic capabilities and time constraints. As a result, students can enroll in flexible terms and make academic progress at their own rate depending on their family and work obligations.
Currently, CBL-related research and development topics are focused on mapping personalized learning processes to facilitate CBL module/content development and personalized learning feedback. U.S. Patent App. Pub. No. 2002/0087346, titled “Utilization of Competencies as Drivers in a Learning Network,” published on Jul. 4, 2002, describes a learning network consisting of competency nodes that can be linked to an individual learner so that the learning patterns of both expert and novice learners' can be accommodated. U.S. Patent App. Pub. No. 2006/0154226 A1, titled “Learning Support Systems,” published on Jul. 13, 2006, describes a hierarchical learning competency map, which can be leveraged to provide personalized learning guidance based on how students perform on various preparation and competency tests. U.S. Pat. No. 6,801,751 B1, titled “Interactive Learning Appliance,” issued on Oct. 5, 2004, describes a system that adapts learning materials to the user's intelligence and other characteristics including user performance information. “Competency-Based Education Programs versus Traditional Data Management” by Sally M. Johnstone (2014) describes CBL programs in the context of data management so they can use the current Student Information System (SIS) and Learning Management System (LMS) platforms to encode student progress in CBL.
Unfortunately, none of these systems provide an algorithm to predict the user's pace of progress and flexible persistence in competency-based education. Furthermore, current predictive models optimized for fixed-term structures are not capable of dealing with such flexible term and learning structures. Thus, there is a need for accommodating such nuanced term structures and learning modalities in predictive algorithms so that timely and accurate predictive insights, along with outreach recommendations, can be provided. Moreover, such predictive insights are of paramount importance in assessing the integrity of the curriculum model and continuously improving CBL courseware structures by understanding the relationships between mastery of competencies and future competency masteries.